RV-WINO: A RISC-V Neural Network Accelerator Based on Winograd Algorithm Fabricated in 55-nm CMOS Process

The rapid evolution of artificial intelligence (AI) in IoT applications necessitates the execution of inference tasks on edge devices. However, the deployment of computation-intensive neural networks on resource-constrained edge systems presents a significant challenge. This brief presents the RV-WINO processor, the first silicon implementation of a RISC-V processor based on the Winograd algorithm for convolution and general matrix multiplication (GEMM) acceleration. The processor incorporates a Winograd module, which significantly reduces multiplication operations during convolutions, leading to a substantial decrease in energy consumption. In addition, the processor includes a matrix multiplication module that reuses the multipliers of the Winograd module, accelerating fully connected and dot product operations in neural networks. The RV-WINO processor fabricated in a 55-nm CMOS process achieves the peak computational performance of 0.95 and 2.39 GOPS in INT32 and INT8 modes, with its peak energy efficiency reaching 112 and 237 GOPS/W. In convolutional neural network (CNN) inference tasks, the execution time is reduced by over 80% compared with the baseline processor.